Algorithmic Trading: A Numbers Game

Algorithmic Trading: A Numbers Game

Algorithmic Trading: A Numbers Game

When the NYSE opened in 1817, individuals scrambled on the trading floor to affirm their position. As time passed, speculative bubbles burst and irrational exuberance started to die down, leading to a change in the public’s perception of the market. Consequently, approaches to investing started to emerge, creating a more diligent investor.

From the candlesticks invented by Homma Munehisa in the 18th century to Richard W. Schabacker, who published books on technical analysis in the 20th century, the change in behaviour was apparent. A new type of investor appeared, armed with technical and fundamental analysis, commercial awareness and an eye for value.

The 21st century has seen the biggest change yet. With the rise of information and technology, the old pattern has been disrupted, and a new financial eco-system created. Individual’s actions and behaviours have changed, in response to new market conditions.

Man vs. Machine

The most prominent development facilitating the change in behaviour is derived from powerful computing, high connectivity speeds and real-time intraday information. The following ingredients gave rise to high-frequency trading.

Its dominance is being manifested by increasing presence in a variety of asset-classes. As a result, a new competitor has entered the gladiator spectacle; the machine.

Due to the short time horizon of investment decision making in HFT, day traders are more likely to experience a change in the competitive landscape. Reacting to certain news or price movements is unlikely to yield trading profits as before. Algorithms react faster, by entering and unwinding positions in milliseconds.

Consequently, such rapid executions of buy/sell orders are likely to distort price signals, making it difficult to enter a particular trade by hand. A primary example of this is the recent flash crash, causing unjustified price swings.

“Temporary or not, flash crashes like the one that took place in August 2015 shake the confidence of individual investors who rely on the public markets to dictate the fundamental value of a company.”

Ted Kaufman

The short-term response seems obvious: ditch intra-day trading and adopt long-term decision making. This approach will accentuate long-term fundamental analysis and macroeconomic factors, rather than short-term inconsistencies within prices, where machines dominate. However, the response of individual investors has been much more profound and radical than one would expect.

A New Type of Investor

The defence mechanism of retail investors and tech pioneers is as innovative as the algorithms themselves. Communities such as Quantopian, WebSim and Quantiacs are removing the sentiment of HFT being an arcane and monopolistic game field. Quantopian has since been dubbed as a “crowdsourced hedge fund”.

The community provides educational resources ranging from data science lectures to back-testing platforms, giving the ability to step into the world of algo trading, without prior knowledge of the market.

The community is likely to expand, through increased funding by Steve Cohen who pledged to invest $250m into Quantopian. The meritocratic approach of the community allows any background to develop skills required to participate in algo trading, rivalling well-established HFT traders.

Despite the perception that algo trading is reserved for savvy computer scientists and mathematicians, Quantopian redefines this sentiment. Amateurs are encouraged to create homemade algorithms, where high-performing code is rewarded.

A recent example includes a 21-year old student. By using the platform, he beat the market, generating a 1.5% return compared to an 8% fall in the S&P index. Such events indicate that the technological awareness is growing amongst the new generation of traders.

The expansion of such communities illustrates that there is an increasing demand to participate and acquire the necessary skillset to create trading strategies based on algorithms, slowly but surely setting a new consensus.

Quantopian’s founder and chief executive John Fawcett said its membership has surged to 60,000 from 35,000 less than a year ago, while QuantConnect’s founder and CEO Jared Broad saw a jump in its membership to 17,000 from 6,000 a year earlier.

The open source nature of such platforms provides the possibility of the development of a new type of investor, where old fundamental knowledge meets innovation to meet the demands of current market conditions. Therefore, overall competition within intraday trading is likely to increase.

Conclusion

The rise of amateur algo-traders will likely lead to severe market disruption. Most disruption stems from peer to peer business models, and algo trading is no exception. Recent hedge-fund performance has been appalling, with investors moving their funds to new ventures.

Quantitative trading houses are gaining momentum, due to stronger preferences in taking a scientific and computerised approach. Within the quantitative segment, communities such as Quantopian are likely to flourish.

Traditional hedge fund models operate on a management fee basis, while the mentioned communities exclude such fees and directly split profits, between the developer and the investor.

Consequently, the elitist bias is removed and anyone will be able to participate in algorithmic trading, due to the sheer amounts of resources and the provision of capital by big names within the industry.

The change in skill set will involve a transition from fundamental valuation methods allied with daily news to number crunching and back-testing. As more trades are carried out by robots, acquiring a new skillset will be a pre-requisite rather than a preference for many investors.

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